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In the era of the Internet of Things (IoT), the demand for accurate positioning services has become increasingly critical, as location-based services (LBSs) depend on users' location data to deliver contextual functionalities. While the Global Positioning System (GPS) is widely regarded as the standard for outdoor localization due to its reliability and comprehensive coverage, its effectiveness in indoor positioning systems (IPSs) is limited by the inherent complexity of indoor environments. This paper examines the various measurement techniques and technological solutions that address the unique challenges posed by indoor environments. We specifically focus on three key aspects: (i) a comparative analysis of the different wireless technologies proposed for IPSs based on various methodologies, (ii) the challenges of IPSs, and (iii) forward-looking strategies for future research. In particular, we provide an in-depth evaluation of current IPSs, assessing them through multidimensional matrices that capture diverse architectural and design considerations, as well as evaluation metrics established in the literature. We further examine the challenges that impede the widespread deployment of IPSs and highlight the potential risk that these systems may not be recognized with a single, universally accepted standard method, unlike GPS for outdoor localization, which serves as the golden standard for positioning. Moreover, we outline several promising approaches that could address the existing challenges of IPSs. These include the application of transfer learning, feature engineering, data fusion, multisensory technologies, hybrid techniques, and ensemble learning methods, all of which hold the potential to significantly enhance the accuracy and reliability of IPSs. By leveraging these advanced methodologies, we aim to improve the overall performance of IPSs, thus paving the way for more robust and dependable LBSs in indoor environments. [ABSTRACT FROM AUTHOR]

Underground mining involves numerous risks, such as collapses, gas leaks, and explosions, posing significant threats to worker safety. In this work, we develop an indoor localization system that uses Bluetooth for coarse positioning and ultrasonic arrays for precision calibration. This system is particularly useful for automated mining operations in underground environments where satellite positioning signals are unavailable. The indoor localization system consists of ultrasonic receiver arrays and an improved multi-transmitter-multi-receiver algorithm, enabling accurate localization within the mining environment. Geometric Dilution of Precision (GDOP) analysis is incorporated to optimize the network layout, and an inertial navigation module is integrated to track the posture of moving objects, enabling precise trajectory determination over large areas, such as coal mines. In the experiment, three traditional methods were compared, and the proposed tracking approach demonstrated a positioning accuracy within 10 cm, reducing error by 20% compared to conventional techniques. This high-precision indoor localization method proves beneficial for underground mining applications. [ABSTRACT FROM AUTHOR]

This study developed a prototype for an electronic travel aids (ETAs) system named Navigation Assisting Vest (NaVest) using Bluetooth Low Energy (BLE) beacons and a smartphone application to address the challenges faced by blind or visually impaired individuals (BVIP) whilst navigating unfamiliar indoor environments. The NaVest employs ultrasonic sensors for obstacle avoidance and indoor navigation functions. Testing and evaluation of the prototype were conducted with 12 BVIP and blindfolded participants in a local university building in the United Arab Emirates. The developed prototype improved the efficiency and safety of navigation tasks, and participants were overall satisfied with the system. Future research should focus on developing ETA systems that combine several functions to help BVIP travel more independently in indoor or outdoor environments. Additionally, ETA systems with multi-language support should be considered to improve accessibility for BVIP. [ABSTRACT FROM AUTHOR]

Recently, UWB-based technology providing centimeter-level accuracy has been developed and widely utilized in indoor real-time location tracking systems. However, location accuracy varies due to factors such as frequency interference, collisions, reflected signals, and whether line-of-sight (LOS) conditions are met, and it can be challenging to ensure high accuracy in specific environments. Fortunately, when anchor positions are fixed, the locations of large obstacles such as columns or furniture remain relatively stable, leading to similar patterns of positioning bias at specific points. This study proposes an algorithm that corrects inaccurate positioning to more closely reflect the actual location based on bias and deviation maps generated using natural neighbor interpolation. Initially, positioning bias and deviations at specific points are sampled, and bias and deviation maps are created using natural neighbor interpolation. During location tracking, the algorithm detects candidate clusters and determines the centroid to estimate the actual location by applying the bias and deviation maps to the measured positions derived through trilateration. To validate the proposed algorithm, experiments were conducted in a non-LOS (NLOS) indoor environment. The results demonstrate that the proposed algorithm can reduce the positioning bias of a UWB-based RTLS by approximately 71.34% compared to an uncalibrated system. [ABSTRACT FROM AUTHOR]

Indoor navigation has attracted significant attention from both academic and industrial perspectives. Indoor positioning is a critical component of indoor navigation. Several solutions or technologies have been proposed, such as Wi-Fi, UWB, and Bluetooth. Among them, Bluetooth Low Energy (BLE) is cost-effective, easily deployable, flexible, and efficient. This paper focuses on indoor positioning solely based on BLE. Motivated by two observations, namely, that (i) involving more anchor nodes can enhance positioning accuracy, and that (ii) narrowing the area for unknown location determination can also lead to improved accuracy, a new distance-based method, the Weighted Centroid of the Convex Polygon (WC-CP), is proposed. While it is generally acknowledged that incorporating more anchor nodes can enhance indoor positioning performance, the current state of the art lacks a robust methodology for selecting and utilizing these nodes. The WC-CP approach addresses this gap by introducing a systematic and efficient method for identifying and employing the most suitable anchor nodes. By avoiding nodes that could potentially introduce significant errors or lead to incorrect localization, our method ensures more accurate and reliable indoor positioning. The efficacy of WC-CP is demonstrated in an indoor environment, achieving an RMSE of 1.35 m. This result shows significant improvements over three state-of-the-art approaches, about 34.15% better than LSBM, 32.50% better than TWCBM, and 30.05% better than ITWCBM. These findings underscore the potential of WC-CP for enhanced accuracy and reliability in indoor positioning based on BLE. [ABSTRACT FROM AUTHOR]

This research presents a detailed methodology for indoor positioning using visible light communication (VLC) technology, focusing on overcoming the limitations of traditional satellite-based navigation systems. The system is based on an optical positioning framework that integrates trilateration techniques with a semi-spherical array of photodiodes, designed to enhance both positional accuracy and orientation estimation. The system effectively estimates the receiver's position and orientation with high precision by utilizing multiple white-light-emitting diodes (LEDs) as transmitters and leveraging angular diversity. The proposed method achieves an average position error of less than 3 cm and an angular accuracy within 10 degrees, demonstrating its robustness even in environments with obstructed line of sight. These results highlight the system's potential for significant indoor positioning accuracy and reliability improvements. [ABSTRACT FROM AUTHOR]

In this paper, a robust algorithm for enhancing indoor positioning accuracy utilizing time difference of arrivals is proposed. Addressing limitations of maximum likelihood estimation and traditional weighted least squares methods, which often suffer from matrix ill-conditioned problem and numerical instability, leading to significant biases and reduced accuracy, we propose a novel bias reduction technique based on QR factorization. Incorporating geometric relationship information, our method improves precision. Through rigorous analysis and simulation under zero-mean white Gaussian noise, the algorithm demonstrates superior performance, overcoming matrix ill-conditioned problem, surpassing traditional methods, and closely aligning with the Cramér-Rao lower bound. [ABSTRACT FROM AUTHOR]

This paper explores the use of few-shot learning in Wi-Fi-based indoor positioning, utilizing convolutional neural networks (CNNs) combined with meta-learning techniques to enhance the accuracy and efficiency of positioning systems. The focus is on addressing the challenge of limited labeled data, a prevalent issue in extensive indoor environments. The study explores various scenarios, comparing the performance of the base CNN and meta-learning models. The meta-learning approach involves few-shot learning tasks, such as three-way N-shot, five-way N-shot, etc., to enhance the model's ability to generalize from limited data. The experiments were conducted across various scenarios, evaluating the performance of the models with different numbers of samples per class (K) after filtering by cosine similarity (FCS) during both the stages of data preprocessing and meta-learning. The scenarios included both base classes and novel classes, with and without meta-learning. The results indicated that the base CNN model achieved varying accuracy levels depending on the scenario and the number of samples per class retained after FCS. Meta-learning performed acceptably in scenarios with fewer samples, which are the distinct datasets pertaining to novel classes. With 20 samples per class, the base CNN achieved an accuracy of 0.80 during the pre-training stage, while meta-learning (three-way one-shot) achieved an accuracy of 0.78 on a new small dataset with novel classes. [ABSTRACT FROM AUTHOR]

Indoor positioning is the key enabling technology for many location-aware applications. As GPS does not work indoors, various solutions are proposed for navigating devices. Among these solutions, Bluetooth low energy (BLE) technology has gained significant attention due to its affordability, low power consumption, and rapid data transmission capabilities, making it highly suitable for indoor positioning. Received signal strength (RSS)-based positioning has been studied intensively for a long time. However, the accuracy of RSS-based positioning can fluctuate due to signal attenuation and environmental factors like crowd density. Angle of arrival (AoA)-based positioning uses angle measurement technology for location devices and can achieve higher precision, but the accuracy may also be affected by radio reflections, diffractions, etc. In this study, a dual-branch convolutional neural network (CNN)-based BLE indoor positioning algorithm integrating RSS and AoA is proposed, which exploits both RSS and AoA to estimate the position of a target. Given the absence of publicly available datasets, we generated our own dataset for this study. Data were collected from each receiver in three different directions, resulting in a total of 2675 records, which included both RSS and AoA measurements. Of these, 1295 records were designated for training purposes. Subsequently, we evaluated our algorithm using the remaining 1380 unseen test records. Our RSS and AoA fusion algorithm yielded a sub-meter accuracy of 0.79 m, which was significantly better than the 1.06 m and 1.67 m obtained when using only the RSS or the AoA method. Compared with the RSS-only and AoA-only solutions, the accuracy was improved by 25.47% and 52.69%, respectively. These results are even close to the latest commercial proprietary system, which represents the state-of-the-art indoor positioning technology. [ABSTRACT FROM AUTHOR]

In recent years, visible light positioning (VLP) techniques have been gaining popularity in research. Among them, the scheme of using a camera as a receiver provides a low-cost, high-precision positioning capability and easy integration with existing multimedia devices and robots. However, the pose changes of the receiver can lead to image distortion and light displacement, significantly increasing positioning errors. Addressing these errors is crucial for enhancing the accuracy of VLP. Most current solutions rely on gyroscopes or Inertial Measurement Units (IMUs) for error optimization, but these approaches often add complexity and cost to the system. To overcome these limitations, we propose a 3D positioning algorithm based on an attention mechanism convolutional neural network (CNN) aimed at reducing the errors caused by angles. We designed experiments and comparisons within a rotation angle range of ±15 degrees. The results demonstrate that the average error for 2D positioning is within 5.74 cm and the height error is within 3.92 cm, and the average error for 3D positioning is within 6.8 cm. Among the four groups of experiments for 3D positioning, compared to the traditional algorithm, the improvements were 7.931 cm, 15.569 cm, 6.004 cm, and 16.506 cm. The experiments indicate that it can be applied to high-precision visible light positioning for single-light imaging. [ABSTRACT FROM AUTHOR]

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Abstract Accurate indoor positioning is the key to the development of the Internet of Things and intelligent devices. In view of GPS-denied indoor environments, we propose to build the indoor local positioning system by using ultra-wide band (UWB) system. In order to enhance the localization accuracy of UWB system, we propose a novel algorithm which integrates the Maximum Correntropy Criterion (MCC) and unscented Kalman filter (UKF) method to reconstruct the measurement distance by using the maximum entropy principle to reduce the influence of outliers and unknown process noise on the smooth effect. Subsequently, the least square (LS) method is implemented to attain the target node (TN) initial position coordinates, and the Taylor algorithm is then performed to further optimize the localization results of the LS method. Lastly, the experimental investigation is conducted to assess the effectiveness and applicability of the developed method via the UWB system in indoor scenarios. The experimental outcomes demonstrate that the developed MCCUKF-LS method can achieve the lowest root mean square error (RMSE), and enhance the positioning accuracy of the TN compared with the LS, KF-LS, and UKF-LS methods. The overall average RMSE of MCCUKF-LS method is reduced by 45.7% contracted with the LS algorithm. The average error of x-, y- and z-axis orientation for the LS method is reduced from 0.074 m, 0.067 m, 0.098 m to 0.036 m, 0.034 m, 0.044 m, and the achieved accuracy in the orientation of the three axes is increased by 51.4%, 49.3% and 55.1% respectively, which reveals that the designed fusion technique is capable of enhancing the positioning accuracy of the TN effectively, providing a new positioning methodology and reference for indoor positioning in GPS-denied environments.

Accurate indoor robot navigation cannot be achieved without reliable indoor positioning techniques. Ultra-wideband (UWB) is among the most dependable techniques currently available. However, the complexity of indoor environments results in signal transmissions that are susceptible to interference from obstacles, which in turn reduces positioning accuracy. Inertial Navigation System (INS) is an autonomous navigation system that is free from interference in indoor environments and unaffected by non-line-of-sight (NLOS) conditions. This paper proposes a new joint INS and UWB positioning method utilizing the Maximum Correntropy Criterion Kalman Filter (MCCKF). This approach effectively cope with the interference of measurement outliers and extend the design of the adaptive mechanisms to enhance the performance of the localization system. For UWB positioning of tag nodes, an improved Particle Swarm Optimization combined with kmeans (PSO-kmeans) method is used to reduce the impact of NLOS errors on positioning. Finally, the INS is calibrated by AMCCKF fused positioning results. The results of simulations and experiments demonstrate that the proposed AMCCKF fusion algorithm effectively suppresses the impact of anomalous measurements, enhances positioning accuracy and robustness, thereby improving its practicality in real-world environments.

Summary: A comparative analysis of 3D positioning error for two different configurations using different layouts of visible light communication (VLC) systems is presented in this paper. The Received Signal Strength (RSS) has been implemented for indoor localization systems using Line‐of‐Sight (LoS) and diffused reflection signals. The room size for configuration‐1 is 5 m × 5 m × 3 m, and the distance between adjacent LEDs is 2.5 m, 2.00 m, and 1.5 m for cases‐1, case‐2, and case‐3, respectively, whereas the room size for configuration‐2 is 7 m × 7 m × 5 m, and the separation between the LEDs is 3.5 m, 3 m, and 2.5 m for their respective cases. Through investigation, it has been shown that when only LS signal is considered, the separation between LEDs may not be an issue because positioning error changes by a very small amount as the separation between LEDs changes. The results show that as the distance between adjacent LEDs decreases, the received signal strength for LoS and L‐R1 signals increases. However, positioning error and BER rise, while the bit rate falls. Furthermore, the positioning error Vs receiver plane height for all three cases in configuration‐1 is the same up to a height of 2.89 m, whereas the positioning error in configuration‐2 is the same up to 4.4 m for all cases. The positioning error for case‐1 decreases as the height in configuration‐1 exceeds 2.89 m. Similarly, after reaching a height of 4.4 m for case‐2, the positioning error in configuration‐2 decreases. The LoS positioning error versus semi angle φ1/2 of the LED as well as the FOV of the receiver has been simulated for different positions of the receiver in configuration‐1. The investigation shows that the minimum positioning error is achieved at φ1/2 and FOV equal to 66.660 for all the positions of the receiver in the room. Thus, before configuring a practical indoor VLC geometrical model, proper VLC configurations such as LED separation, FOV of the receiver, semi angle of LED, and receiver height should be chosen based on the room dimensions. [ABSTRACT FROM AUTHOR]

Abstract With the development of wireless communication technology, Ultra-Wideband (UWB) has become an important solution for indoor positioning. In complex indoor environments, the influence of non-line-of-sight (NLOS) factors leads to increased positioning errors. To improve the positioning accuracy, fuzzy iterative self-organizing data analysis clustering algorithm (ISODATA) is introduced to process a large amount of UWB data to reduce the influence of NLOS factors, and to stabilize positioning error within 2 cm, enhances the accuracy of the positioning system. To further improve the running efficiency of the algorithm, FPGA is used to accelerate the key computational part of the algorithm, compared with running on the MATLAB platform, which improves the speed about 100 times, enhances the algorithm’s computational speed dramatically.

Unmanned intelligent vehicles are exposed to high risks of network attack, hardware attack, operating system attack and software attack. They are susceptible to physical or remote security attacks, causing it to deviate from the delivery trajectory and fail the delivery task, or even be manipulated to disrupt normal operation of the factory. To address this problem, a dual-verified secure localization method for unmanned intelligent vehicles was proposed. The existing Wi-Fi network infrastructure was utilized by the vehicles for fingerprinting localization and a feature fusion strategy was designed to realize the dynamic fusion of Wi-Fi and magnetic field fingerprints. Multiple environmental monitoring points were deployed to collect the sound signals made by vehicles to calculate the position based on time difference of arrival and spatial segmentation method. Then the location reported by the vehicle was compared with the result of monitoring points for verification. Once an abnormal position was detected, an alert would be issued, ensuring the normal operation of the unmanned intelligent vehicles. The experimental results in the real indoor scenarios show that the proposed method can effectively track the positions of the target unmanned intelligent vehicle, and the positioning accuracy is better than existing benchmark algorithms.

With the development of wireless communication technology, Ultra-Wideband (UWB) has become an important solution for indoor positioning. In complex indoor environments, the influence of non-line-of-sight (NLOS) factors leads to increased positioning errors. To improve the positioning accuracy, fuzzy iterative self-organizing data analysis clustering algorithm (ISODATA) is introduced to process a large amount of UWB data to reduce the influence of NLOS factors, and to stabilize positioning error within 2 cm, enhances the accuracy of the positioning system. To further improve the running efficiency of the algorithm, FPGA is used to accelerate the key computational part of the algorithm, compared with running on the MATLAB platform, which improves the speed about 100 times, enhances the algorithm's computational speed dramatically. [ABSTRACT FROM AUTHOR]

This study presents a novel approach to indoor positioning leveraging radio frequency identification (RFID) technology based on received signal strength indication (RSSI). The proposed methodology integrates Gaussian Kalman filtering for effective signal preprocessing and a time-distributed auto encoder-gated recurrent unit (TAE-GRU) model for precise location prediction. Addressing the prevalent challenges of low accuracy and extended localization times in current systems, the proposed method significantly enhances the preprocessing of RSSI data and effectively captures the temporal relationships inherent in the data. Experimental validation demonstrates that the proposed approach achieves a 75.9% improvement in localization accuracy over simple neural network methods and markedly enhances the speed of localization, thereby proving its practical applicability in real-world indoor localization scenarios. [ABSTRACT FROM AUTHOR]

With the addition of Bluetooth AOA/AOD direction-finding capabilities in the Bluetooth 5.1 protocol and the introduction of antenna array technology into the Bluetooth platform to further enhance positioning accuracy, Bluetooth has gradually become a research hotspot in the field of indoor positioning due to its standard protocol specifications, rich application ecosystem, and outstanding advantages such as low power consumption and low cost compared to other indoor positioning technologies. However, current indoor positioning based on Bluetooth AOA/AOD suffers from overly simplistic core algorithm implementations. When facing different application scenarios, the standalone AOA or AOD algorithms exhibit weak applicability, and they also encounter challenges such as poor positioning accuracy, insufficient real-time performance, and significant effects of multipath propagation. These existing problems and deficiencies render Bluetooth lacking an efficient implementation solution for indoor positioning. Therefore, this paper proposes a study on Bluetooth AOA and AOD indoor positioning algorithms. Through an analysis of the principles of Bluetooth's newly added direction-finding functionality and combined with research on array signal DOA estimation algorithms, the paper ultimately integrates the least squares algorithm to optimize positioning errors in terms of accuracy and incorporates an anti-multipath interference algorithm to address the impacts of multipath effects in different scenarios. Experimental testing demonstrates that the indoor positioning algorithms applicable to Bluetooth AOA and AOD can effectively mitigate accuracy errors and overcome multipath effects, exhibiting strong applicability and significant improvements in real-time performance. [ABSTRACT FROM AUTHOR]

In the ultra-wideband indoor positioning sceneraio, the non-line of sight (NLOS) propagation may be caused by obstacles, which may lead to the deviation of ranging value and affect the positioning precision. Therefore, we propose a NLOS identification and error regression positioning algorithm based on light gradient boosting machine (LightGBM). Firstly, ReliefF algorithm combined with Spearman correlation coefficient is used to analyze the feature correlation, and eight channel features such as total channel impulse response power and standard deviation of noise are selected as NLOS identification features. Then, we adopt genetic algorithm to optimize the hyperparameters of LightGBM for NLOS identification. On this basis, the proposed error regression model based on convolutional neural network (CNN) combined with LightGBM is used to correct the ranging results, so as to achieve high-precision positioning. Through the verification on the public dataset, the NLOS identification accuracy reached 91.8%, and the positioning precision is improved by 45 cm after correcting the ranging results. [ABSTRACT FROM AUTHOR]

The present technology, based on laser and visual SLAM navigation and positioning, does not apply to the event of a fire in a room where there is a large amount of smoke, for its increasingly obvious defects. In addition, traditional track deduction technology based on photoelectric encoder has accumulated error, and noise disturbance exists in the INS inertial navigation measurement technology, and the UWB positioning technology is vulnerable to NLOS disturbance caused by site occlusion. To solve the problem of accurate positioning of Autonomous Mobile fire-fighting robot in smoke scenes, the system design is optimized by adopting the following methods: using IMU-assisted residual chi-square criterion to detect whether there is NLOS in UWB, introducing IMU instantaneous compensation positioning data and adopting Chan algorithm fitting of the second multiplication to ensure the stability and accuracy of UWB data; meanwhile, a tight combination model of navigation and positioning is designed: via the improved Kalman filter algorithm, fused with the magnetic encoder track estimation pose, UWB absolute and IMU heading angle pose to realize the accurate positioning of the fire robot in the smoke scene. Finally, the fusion simulation model and algorithm are verified by MATLAB, as it shows, the method has an average positioning accuracy of 98.63% in the X-axis direction, 99.52% in the Y-axis direction, and 97.24% in the heading angle, which solves the inherent physical defects of a single positioning sensor and serves as a reliable and accurate solution for indoor robot positioning. [ABSTRACT FROM AUTHOR]

The fields of indoor localization and positioning have seen extensive research in recent years. Their scientific soundness is of great importance, as information about an entity's location in indoor environments can lead to innovative services and products. Various techniques and frameworks have been proposed, some of which are already in practical use. This article emphasizes the value of indoor localization data and proposes the adoption of a new virtual field known as the 'Transactional Area Network' (TAN). By presenting a custom yet simple real-time, peer-to-peer (and therefore decentralized) software implementation that provides positioning information to users via their smart devices, this article demonstrates the potential value of TAN. Finally, it explores how TAN can increase the adoption rate of indoor positioning applications, enhance interactions between people in nearby locations and therefore amplify data generation. [ABSTRACT FROM AUTHOR]

The ultra-wideband (UWB) technology has been increasingly recognized as an efficacious strategy for Indoor Positioning Systems (IPSs). However, the accuracy of the UWB system can be severely degraded by non-line-of-sight (NLOS) errors. In this study, we proposed a new method to reduce the UWB positioning error in such an indoor environment. We developed a system consisting of a Robotic Total Station (RTS), four UWB base stations, a moving target (including a prism and a UWB tag), and a PC. The observed coordinates of the moving target, captured using millimeter precision from an RTS device, served as the ground truth for calculating the positioning errors of the UWB tag. In a significant NLOS scenario, the UWB's three-dimensional positioning error was identified to exceed the nominal value declared by the manufacturer by a factor of more than three. A detailed analysis revealed that each coordinate component's error distribution pattern demonstrated considerable variance. To reduce the NLOS error, we designed a combined multilayer neural network that simultaneously fits errors on all three coordinate components and three separate multilayer networks, each dedicated to optimizing errors on a single coordinate component. All networks were trained and verified by benchmark errors obtained from the RTS. The results showed that neural networks outperform the traditional methods, attributed to their strong nonlinear modelling ability, thereby significantly improving the external accuracy by an average reduction in RMSE by 61% and 72%. It is evident that the proposed separate networks would be more suitable for NLOS positioning problems than a combined network. [ABSTRACT FROM AUTHOR]

Composite indoor human activity recognition is very important in elderly health monitoring and is more difficult than identifying individual human movements. This article proposes a sensor-based human indoor activity recognition method that integrates indoor positioning. Convolutional neural networks are used to extract spatial information contained in geomagnetic sensors and ambient light sensors, while transform encoders are used to extract temporal motion features collected by gyroscopes and accelerometers. We established an indoor activity recognition model with a multimodal feature fusion structure. In order to explore the possibility of using only smartphones to complete the above tasks, we collected and established a multisensor indoor activity dataset. Extensive experiments verified the effectiveness of the proposed method. Compared with algorithms that do not consider the location information, our method has a 13.65% improvement in recognition accuracy. [ABSTRACT FROM AUTHOR]

Indoor positioning has been extensively studied for at least the past twenty years. In the list of the most common solutions, those based on the Received Strength Signal Indicator (RSSI) have gained importance due to the simplicity of RSSI as well as the fact that it is available in several wireless sensor networks. In this work, we propose SeALS (Selection Strategy of Access Points with Least Squares Estimation), a new RSSI-based indoor positioning system using Bluetooth Low-Energy (BLE) access points, whose accuracy is improved by a new selection strategy of collected RSSI combined with the Ordinary Least Squares (OLS) estimation method. The main advantage of the proposed solution is the fact that it requires less time in the training phase allied with better system accuracy if compared to traditional methods. The proposed system is validated in a large-scale, real-world scenario, and the obtained results for the positioning error are reduced by up to 13% concerning the pure OLS method, and by up to 30% concerning the widely deployed K-Nearest Neighbors technique. [ABSTRACT FROM AUTHOR]

Global Positioning System (GPS) is a famous technology around the world in identifying the real time precise location of any object with the assistance of satellites. The most common application of GPS is the use of outdoor maps. GPS offers efficient, scalable and cost-effective location services. However, this technology is not reliable when the position is in an indoor environment. The signal is very weak or totally lost due to signal attenuation and multipath effects. Among the indoor positioning technologies, WLAN is the most convenient and cost effective. In recent research, machine learning algorithms have become popular and utilized in wireless indoor positioning to achieve better performance. In this paper, different machine learning algorithms are employed to classify different positions in the real-world environment (e.g., Ixora Apartment - House and Multimedia University Malacca - FIST building). Received Signal Strength Indication (RSSI) is collected at each reference point. This data is then used to train the model with hyperparameter tuning. Based on the experiment result, Random Forest achieved 82% accuracy in Ixora Apartment and 84% accuracy in one of the buildings in Multimedia University Malacca. These results outperformed the other models, i.e., K-Nearest Neighbors (KNN) and Support Vector Machine (SVM). [ABSTRACT FROM AUTHOR]

Indoor position fingerprint-based location estimation methods have been widely used by applications on smartphones. In these localization estimation methods, it is very popular to use the RSSI (Received Signal Strength Indication) of signals to represent the position fingerprint. This paper proposes the design of a particle filter for reducing the estimation error of the machine learning-based indoor BLE location fingerprinting method. Unlike the general particle filter, taking into account the distance, the proposed system designs improved likelihood functions, considering the coordinates based on fingerprint points using mean and variance of RSSI values, combining the particle filter with the k-NN (k-Nearest Neighbor) algorithm to realize the reduction in indoor positioning error. The initial position is estimated by the position fingerprinting method based on the machine learning method. By comparing the fingerprint method based on k-NN with general particle filter processing, and the fingerprint estimation method based on only k-NN or SVM (Support Vector Machine), experiment results showed that the proposed method has a smaller minimum error and a better average error than the conventional method. [ABSTRACT FROM AUTHOR]

Combining inertial navigation system (INS) and ultra-wideband (UWB) technologies can effectively compensate for their respective shortcomings, thus significantly enhancing the accuracy of indoor positioning systems. However, in the process of fusing these two technologies, signal fading under non-line-of-sight (NLOS) conditions, multipath effects, and errors accumulated by the INS over a long period of time are still key issues that need to be addressed. To cope with these challenges, a new fusion localization algorithm is proposed in this study. The algorithm employs a combination of fuzzy C-mean (FCM) and K-Medoids algorithms for UWB for position computation on the one hand, and an Implicit Unscented Particle Filter (IUPF)-enhanced INS for navigation information processing on the other. In addition, based on the INS error equation, this algorithm realizes the effective fusion of UWB and INS positioning information through the Minimum Error Entropy Extended Kalman Filter (MEE-EKF) technique. This integrated approach significantly improves the accuracy and stability when dealing with the localization problem in complex indoor environments. After simulation experiments under different noise conditions and real environment experiments, the algorithm proposed in this study shows significant advantages in terms of localization performance over the traditional UWB/INS localization methods in recent years. In real experiments, the algorithm achieves an average of 36.15% improvement in positioning accuracy.

Pedestrian Dead Reckoning (PDR) plays an important role in multi-sensor fusion of indoor positioning due to its autonomy and continuity advantages. The robustness of PDR significantly impacts indoor positioning accuracy, but various pedestrians and mobility contexts pose challenges for reliable step detection and accurate step length estimation. This paper proposes a context-assisted personalized PDR localization solution to address these challenges. Firstly, by exploiting temporal and frequency domain features, an enhanced step detection method is developed to mitigate false step detection, especially during unfavorable actions of pedestrians. Subsequently, a personalized step length model is proposed, and its parameters are dynamically updated online using other high-precision sensors available within a multi-sensor fusion positioning solution. Moreover, the personalized step length model is further refined using mobility context knowledge. Finally, a novel context-assisted pedestrian velocity model is established for PDR localization to enhance positioning accuracy, particularly when there are changes in mobility contexts. The results demonstrate that the robustness of step detection is improved, and the false detection rate is reduced from 13% to 2%. For various smartphone users, the proposed context-assisted personalized step length model exhibits a relative error of 2.01%, in contrast to 7.06% observed with the traditional flat model. Consequently, the accuracy of walking distance is enhanced from 92.2% to 98.9%, and the PDR localization error is reduced from 2.49 m to 1.63 m. Importantly, the proposed solution exhibits more robust and consistent performance across different pedestrians, smartphone models, and challenging mobility contexts.

The inertial navigation system (INS), which is frequently used in emergency rescue operations and other situations, has the benefits of not relying on infrastructure, high positioning frequency, and strong real-time performance. However, the intricate and unpredictable pedestrian motion patterns lead the INS localization error to significantly diverge with time. This paper aims to enhance the accuracy of zero-velocity interval (ZVI) detection and reduce the heading and altitude drift of foot-mounted INS via deep learning and equation constraint of dual feet. Aiming at the observational noise problem of low-cost inertial sensors, we utilize a denoising autoencoder to automatically eliminate the inherent noise. Aiming at the problem that inaccurate detection of the ZVI detection results in obvious displacement error, we propose a sample-level ZVI detection algorithm based on the U-Net neural network, which effectively solves the problem of mislabeling caused by sliding windows. Aiming at the problem that Zero-Velocity Update (ZUPT) cannot suppress heading and altitude error, we propose a bipedal INS method based on the equation constraint and ellipsoid constraint, which uses foot-to-foot distance as a new observation to correct heading and altitude error. We conduct extensive and well-designed experiments to evaluate the performance of the proposed method. The experimental results indicate that the position error of our proposed method did not exceed 0.83% of the total traveled distance.